Advanced Technology Lab Methods

Utilizing Single cells by Combinatorial Indexing

By performing large-scale single cell molecular profiling experiments in a centralized lab, we aim to produce high quality, consistent data while keeping costs for individual researchers to a minimum. sci-RNA-seq or single cells by combinatorial indexing leverages a ‘split and pool’ molecular barcoding strategy to obviate the need to capture single cells in individual compartments. Sci technology allows us to profile nucleic acids in single cells on a massive scale because it scales sub-linearly with cost.

Protocols

sci-RNA-seq Protocol

sci-RNA-seq3 is an ultra high throughput (2 million cells per run) single cell/nucleus RNA sequencing technique: Cao et al, Nature 20 Feb 2019. It builds on other single cell combinatorial indexing (sci-) assays that we and/or colleagues have developed.

Learn more about the protocol here >

Is this method right for you?

How Our Single Cell Sequencing Service Works

We now offer a suite of services for single nucleus sequencing and data interpretation. Our tools support multiplexed single nucleus RNA-seq, from nuclei isolation through single cell sequencing and analysis. We employ comprehensive quality control measures at each step, including: quality control of sample input, sample isolation, sci-RNA-seq experiments and analyses. Our bioinformatics pipelines include breakdown of sequence to Cell by Gene by Count matrices, advanced quality control metrics and automated cell typing. See how the single cell sequencing lab can help optimize your efforts.

Thousands to Millions of Nuclei

The sci-RNA-seq method returns thousands of cells per sample making this a key technology for probing tissue complexity and understanding how gene expression and cell-type composition change under different conditions. Sci-RNA-seq experiments scale sublinearly with cost; each full experiment returns hundreds of thousands of nuclei. Investigators can choose to profile from thousands to millions of nuclei. The single cell sequencing team is optimizing isolation and sequencing protocols for a diverse range of samples and experiments. With inputs as low as 0.5 x 10^6 cells or 20mg of tissue, we perform sequencing at scale for many systems.

Optimized Protocols for Various Model Systems

Model Organisms (D. melanogaster, C. elegans, D. rerio)
Embryonic Mammalian Tissues
Adult Mouse Tissues
Cell-based systems (cell lines, primary B or T cells, organoids…)
Xenografts
Other model organisms with STAR alignments and ENSEMBL annotations

Suite of Services

sci-RNA-sequencing

cao protocol The Advanced Technology Lab is adept at isolating nuclei from a wide range of tissues and cell types. Each scaled sci-RNA-seq experiment includes a diverse set of samples, all loaded as fixed nuclei. The power of the combinatorial indexing approach allows us to combine many experiments into one: we are able to profile large cell atlases as well as tissue- or cell-specific projects. Our experimental service includes experimental consultation, multiple quality control steps, and production level sci-RNA-seq runs.

Data processing pipeline

bioinformatics dashboard The Brotman Baty bioinformatics team preprocesses all single cell sequencing data and returns data ready to use, including a web dashboard that shows the results of some basic quality control and analysis. We return all raw data as well as a premade Cell Data Set (cds) object to use with Monocle3 , a well-developed toolkit for analyzing single-cell gene expression experiments. Basic Monocle3 analyses and quality control steps are outlined in the web dashboard, allowing investigators with limited R programming experience to jump right into data analysis.

Fred Hutch Center for Data Visualization

FH data visualization

Human survival has always hinged on the ability to translate visual signals into patterns, trends, and correlations. In the age of big data, this aptitude has proven effective at turning data into life-saving discoveries. The Center for Data Visualization combines software, statistics, and science through storytelling to illuminate, inspire, and most importantly, lead to new biologic and therapeutic insights. We aim to develop open-source, web-based tools to assist researchers in the exploration of multi-modal Single Cell experiments. Learn more here >

Single Cell Research Services

Contact us to discuss how your research goals can be addressed using sci-RNA-seq, and to determine whether your samples will be compatible with the Advanced Technology Lab protocol. We offer optimization for in-house sample preparation protocols for a cost, or alternatively can provide guidance on optimization efforts that can be performed in your own lab.

Contact the team with questions at scibats@u.washington.edu

Tools

Building Community

The Brotman Baty Institute brings together a community of individuals and labs conducting groundbreaking research in areas relevant to precision medicine. To this end, the Brotman Baty Advanced Technology Lab is working to provide single-cell RNA sequencing as a service for this research community. Using a powerful method, combinatorial indexing, we can measure transcription across millions of individual nuclei in parallel. See below for single cell resources, including computational tools to analyze these complex data and single cell protocols and publications developed by Brotman Baty member labs.

Resources

R toolkit for analyzing single-cell gene expression

The Monocle 3 R package is a toolkit for analyzing single-cell gene expression experiments. Monocle 3 can help you visualize your data, identify changes in gene expression and explore cellular development. Monocle 3 performs three main types of analysis including clustering and counting cells, constructing single-cell trajectories, and identifying differential expression. For more information about Monocle 3, explore our website and our publications. Monocle3 >

Semi-automated cell type classification

Garnett is a software package that facilitates semi-automated cell type classification from single-cell expression data. Garnett works by taking single-cell data, along with a cell type definition (marker) file and training a regression-based classifier. Once a classifier is trained for a tissue/sample type, it can be applied to classify future datasets from similar tissues. In addition to describing training and classifying functions, the Garnett website aims to be a repository of previously trained classifiers. For more information about Garnett, explore our website and our publication. Garnett >

Publications

Nature - July 5, 2019

Comprehensive single-cell transcriptome lineages of a proto-vertebrate

Ascidian embryos highlight the importance of cell lineages in animal development. As simple proto-vertebrates, they also provide insights into the evolutionary origins of cell types such as cranial placodes and neural crest cells. Here we have determined single-cell transcriptomes for more than 90,000 cells that span the entirety of development-from the onset of gastrulation to swimming tadpoles-in Ciona intestinalis. Owing to the small numbers of cells in ascidian embryos, this represents an average of over 12-fold coverage for every cell at every stage of development. We used single-cell transcriptome trajectories to construct virtual cell-lineage maps and provisional gene networks for 41 neural subtypes that comprise the larval nervous system. We summarize several applications of these datasets, including annotating the synaptome of swimming tadpoles and tracing the evolutionary origin of cell types such as the vertebrate telencephalon. Read more >

Science - September 28, 2018

Joint profiling of chromatin accessibility and gene expression in thousands of single cells

Although we can increasingly measure transcription, chromatin, methylation, and other aspects of molecular biology at single-cell resolution, most assays survey only one aspect of cellular biology. Here we describe sci-CAR, a combinatorial indexing-based coassay that jointly profiles chromatin accessibility and mRNA (CAR) in each of thousands of single cells. As a proof of concept, we apply sci-CAR to 4825 cells, including a time series of dexamethasone treatment, as well as to 11,296 cells from the adult mouse kidney. With the resulting data, we compare the pseudotemporal dynamics of chromatin accessibility and gene expression, reconstruct the chromatin accessibility profiles of cell types defined by RNA profiles, and link cis-regulatory sites to their target genes on the basis of the covariance of chromatin accessibility and transcription across large numbers of single cells. Read more >

Nature - March 14, 2018

The cis-regulatory dynamics of embryonic development at single-cell resolution

Understanding how gene regulatory networks control the progressive restriction of cell fates is a long-standing challenge. Recent advances in measuring gene expression in single cells are providing new insights into lineage commitment. However, the regulatory events underlying these changes remain unclear. Here we investigate the dynamics of chromatin regulatory landscapes during embryogenesis at single-cell resolution. Using single-cell combinatorial indexing assay for transposase accessible chromatin with sequencing (sci-ATAC-seq)1, we profiled chromatin accessibility in over 20,000 single nuclei from fixed Drosophila melanogaster embryos spanning three landmark embryonic stages: 2–4 h after egg laying (predominantly stage 5 blastoderm nuclei), when each embryo comprises around 6,000 multipotent cells; 6–8 h after egg laying (predominantly stage 10–11), to capture a midpoint in embryonic development when major lineages in the mesoderm and ectoderm are specified; and 10–12 h after egg laying (predominantly stage 13), when each of the embryo’s more than 20,000 cells are undergoing terminal differentiation. Read more >

Science - August 18, 2017

Comprehensive single-cell transcriptional profiling of a multicellular organism

To resolve cellular heterogeneity, we developed a combinatorial indexing strategy to profile the transcriptomes of single cells or nuclei, termed sci-RNA-seq (single-cell combinatorial indexing RNA sequencing). We applied sci-RNA-seq to profile nearly 50,000 cells from the nematode Caenorhabditis elegans at the L2 larval stage, which provided >50-fold "shotgun" cellular coverage of its somatic cell composition. From these data, we defined consensus expression profiles for 27 cell types and recovered rare neuronal cell types corresponding to as few as one or two cells in the L2 worm. We integrated these profiles with whole-animal chromatin immunoprecipitation sequencing data to deconvolve the cell type-specific effects of transcription factors. The data generated by sci-RNA-seq constitute a powerful resource for nematode biology and foreshadow similar atlases for other organisms. Read more >

Science - May 22, 2015

Multiplex single cell profiling of chromatin accessibility by combinatorial cellular indexing

Technical advances have enabled the collection of genome and transcriptome data sets with single-cell resolution. However, single-cell characterization of the epigenome has remained challenging. Furthermore, because cells must be physically separated before biochemical processing, conventional single-cell preparatory methods scale linearly. We applied combinatorial cellular indexing to measure chromatin accessibility in thousands of single cells per assay, circumventing the need for compartmentalization of individual cells. We report chromatin accessibility profiles from more than 15,000 single cells and use these data to cluster cells on the basis of chromatin accessibility landscapes. We identify modules of coordinately regulated chromatin accessibility at the level of single cells both between and within cell types, with a scalable method that may accelerate progress toward a human cell atlas.

The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells

Defining the transcriptional dynamics of a temporal process such as cell differentiation is challenging owing to the high variability in gene expression between individual cells. Time-series gene expression analyses of bulk cells have difficulty distinguishing early and late phases of a transcriptional cascade or identifying rare subpopulations of cells, and single-cell proteomic methods rely on a priori knowledge of key distinguishing markers. Here we describe Monocle, an unsupervised algorithm that increases the temporal resolution of transcriptome dynamics using single-cell RNA-Seq data collected at multiple time points. Applied to the differentiation of primary human myoblasts, Monocle revealed switch-like changes in expression of key regulatory factors, sequential waves of gene regulation, and expression of regulators that were not known to act in differentiation. We validated some of these predicted regulators in a loss-of function screen. Monocle can in principle be used to recover single-cell gene expression kinetics from a wide array of cellular processes, including differentiation, proliferation and oncogenic transformation.